In present day, Signal Integrity analysis is concerned with jitter (timing uncertainties) and noise (voltage uncertainties) performance of data channel(s) or clock circuits. These two dimensions, in which the electronic data channel or clock manifests are equally important and excessive jitter or noise can lead to data channel malfunction. The two phenomena are actually intertwined as increased noise generally leads to increased jitter, and jitter can result in increased noise.
This kind of analysis and investigation is generally categorized as signal integrity (SI) analysis. In recent history, most of the SI focus has been on jitter analysis. Much work has been done to devise methods (prior art) to decompose jitter into component parts which permit a better understanding of the nature of this “uncertainty in timing” we call jitter. Some attention has been paid to similar decomposing the noise of the same circuits, but this area has so far been underexploited.
Furthermore as more and more serial data channels are packed into close proximity the issue of “crosstalk” or unwanted interference between circuits has become a recognized problem to be addressed. There are a number of ways that this unwanted interference can affect and impair the performance, depending on the physics of the interference. One such mechanism is electromagnetic coupling. That is the propagation of fields arising from the rapidly changing currents in printed circuit conductors. The principle manifestation of “crosstalk” of this kind is “noise” by nature. “Noise” may be defined as any undesired pollution of a transmitted signal due to electronic noise (as defined in the industry) intrinsic to a data channel's circuitry, but including any effects induced by neighboring active signals, whether they are other data channels, or simply other dynamic electronic signals (or voltage sources) in the vicinity of a data channel under observation. Some “crosstalk” from other signals is understood to be included in the “noise” which can degrade and impair a data channel, and as such is undesirable. In light of the interest in crosstalk, a closer look at noise analysis is a logical extension of SI analysis. “Noise” in general for a data channel will encompass both the intrinsic noise of the channel, as well as any perturbations induced by the aforementioned “crosstalk” from whatever other signals are in the vicinity. To further complicate matters, whatever measurement instrumentation is employed to “observe” the data channel and other neighboring signals has its own “noise” contributions, and this measurement noise is as important to consider as either the intrinsic or the “crosstalk” noise components. To thoroughly dissect and analyze noise and whatever crosstalk may be present, it is important to develop a methodology that provides the most clear characterization of which parts of the “noise” are dependent on the average signal shape, which parts are bounded, which parts are not, and to isolate all that is not systematically related to the signal itself, so that it may be analyzed in relation to candidate crosstalk signals, for the purpose of identifying the source of the crosstalk.
The fundamental nature of an oscilloscope measurement (or waveform recording instrument) is one that “samples” at some nominally uniform time intervals the voltage of a signal which is presented to it. The voltage is a varying function over time for any data channel of interest, but even lacking a data channel, any voltage source has variations over time which are random and which are commonly known as “noise”. The sources of noise are rooted in the physics of whatever circuit is being observed. There are many references on this subject easily available in text books and on the Internet.
A tool commonly used in studying noise and jitter is called an “eye” diagram. Such diagrams have been in existence for many years and offer a 2 dimensional approximation of the “probability density” for the signals under analysis (2D eye diagrams). These 2D eye diagrams have a number of weaknesses which are seldom discussed. One problem is that they continue to change as more and more data contributes to the eye diagram, and there is no easy way to know when you have “enough” data. This evolution of an eye diagram is due to the simple nature of random noise. It is well known in statistics that the expected value of the peak-to-peak of a Gaussian or Gaussian-like distribution of an observed set of events depends on the number of events observed. As more and more events are observed, the width of the observed distribution broadens. For example, FIG. 1 shows an eye diagram with a nine thousand UI eye [1] as it would look after nine thousand unit interval (UI) have been accumulated. Furthermore, FIG. 2 shows an eye diagram with a five million UI eye [3] as it would look after five million UI have been accumulated. As expected the extents of the populated regions of the eye have grown as more UI are accumulated. As such, two eye diagrams from a different number of UI cannot be compared directly.
Often eye diagrams are used to perform a “mask” test, wherein a polygon or polygons are used to define regions of exclusion not to be touched by the points in the eye diagram. The problem of course, is how much data is needed for a valid mask test, because the probability of a mask violation depends on how many chances the signal under test is given to violate the mask. This is fundamentally a consequence of the eye diagram being non-convergent. There are regions of the 5 million UI eye that are impacted [4], whereas for the 9 thousand UI eye the same region is not impacted [2].
One approach to solve this problem is to try to estimate from the eye diagram a “contour plot” or a 2-dimensional representation. The contour plot is a well known concept. It is supposed to represent the absolute probability of the signal under observation to touch a given coordinate in the eye diagram coordinates. Methods for this kind of calculation exist today. For oscilloscopes these methods suffer from ambiguity in calculating probabilities from an already formed eye. In particular in the region of the contributions from rising edges and falling edges contributions to the eye diagram make it impossible to know if the trajectory of the signal under test that produced that point was earlier or later. Furthermore in an already formed eye diagram, the separation of vertical (noise) contributions from horizontal (jitter) is not possible. For example, if one wanted to compensate the eye diagram for the contribution of the measuring instrument's inherent noise, one cannot. Contour plots can also be generated by a Bit Error Rate Tester that is specially equipped for this task. This same shortcoming applies, in that the noise and jitter inherent in the instrument cannot be effectively removed from such a contour plot. It is notable that in the SI analysis prior-art there is a consortium based software tool referred to as “Stat-Eye”. This tool can produce eye diagrams based on assumptions about noise and jitter and these objects have a different set of problems while addressing some of the defects in ordinary eye diagrams. These are essentially predictive tools dependent on electronic models and conscious assertions made by the user of the tool.
In general, in current SI analysis, there is no way to independently analyze the spectrum on time-domain of “only” the non-deterministic part of the noise, without the spectrum of the signal itself present in the spectrum.
The inventor of the present invention has determined that both contour plots and eye diagrams would be more useful for comparing test cases where crosstalk is present compared to cases where crosstalk is not present, if the above shortcomings could be overcome. The compensation is important to minimize the impact of the measuring instrument, and improving the quality and precision of the contour plot would be very beneficial.
Current SI methods do permit characterization of a data pattern's systematic trajectory, or shape through every bit or UI of the test pattern. This is accomplished via resampling data to have exactly N resampled points and forming a signal average from these resampled points. Such methods are standard in industry standards serial-attached-SCSI (SAS) for the purpose of estimating total waveform distortion penalty (TWDP). However these methods only supply the shape or trajectory of the signal under test, either as a function of position within a repeating sequence of test data, or as defined by the surrounding local sequence of data states.
Therefore, the inventor of the present invention has determined that what is needed is:                1. A convergent form of the eye diagram. That is one which does not change significantly as more data is accumulated.        2. A means to compensate the eye diagram for the noise inherent in the measuring instrument.        3. A means to overcome the inability of an oscilloscope to produce a contour plot which extends outside the central region of the eye.        4. A means to produce a contour plot which is compensated for the inherent noise of the measuring instrument.        5. Good methods for visualizing effects of crosstalk.        